Regularized Spline with Tension was used to interpolate two data sets representing radioactivity measurements at 200 locations. A cross-validation analysis showed that the size of the training data sets was too low to find optimal parameters using the cross-validation procedure. The resulting surfaces were strongly smoothed and less realistic than expected. Therefore empirical interpolation parameters were used to interpolate the data. Despite the fact that this empirical selection did not produced interpolation results with a lower overall predictive error, it preserved better local fluctuations and anomalies of the phenomenon. The detection of these features is important in radioactivity monitoring and emergency situations. The poor reliability of cross-validation was also confirmed by evaluation data set. It was concluded that the optimization of interpolation parameters cannot rely on cross-validation when the modeled phenomenon is not sufficiently sampled. The sampling density should be sufficient to represent spatial variations of the phenomenon and, at the same time, allow the optimization of interpolation parameters using automated procedures.

Regularized Spline with Tension was used to interpolate two data sets representing radioactivity measurements at 200 locations. A cross-validation analysis showed that the size of the training data sets was too low to find optimal parameters using the cross-validation procedure. The resulting surfaces were strongly smoothed and less realistic than expected. Therefore empirical interpolation parameters were used to interpolate the data. Despite the fact that this empirical selection did not produced interpolation results with a lower overall predictive error, it preserved better local fluctuations and anomalies of the phenomenon. The detection of these features is important in radioactivity monitoring and emergency situations. The poor reliability of cross-validation was also confirmed by evaluation data set. It was concluded that the optimization of interpolation parameters cannot rely on cross-validation when the modeled phenomenon is not sufficiently sampled. The sampling density should be sufficient to represent spatial variations of the phenomenon and, at the same time, allow the optimization of interpolation parameters using automated procedures.

Regularized Spline with Tension was used to interpolate two data sets representing radioactivity measurements at 200 locations. A cross-validation analysis showed that the size of the training data sets was too low to find optimal parameters using the cross-validation procedure. The resulting surfaces were strongly smoothed and less realistic than expected. Therefore empirical interpolation parameters were used to interpolate the data. Despite the fact that this empirical selection did not produced interpolation results with a lower overall predictive error, it preserved better local fluctuations and anomalies of the phenomenon. The detection of these features is important in radioactivity monitoring and emergency situations. The poor reliability of cross-validation was also confirmed by evaluation data set. It was concluded that the optimization of interpolation parameters cannot rely on cross-validation when the modeled phenomenon is not sufficiently sampled. The sampling density should be sufficient to represent spatial variations of the phenomenon and, at the same time, allow the optimization of interpolation parameters using automated procedures.